Added AWS-Bedrock LLM setup section

[cheriepwoo]

Also added link to Flowise deployment on AWS.

[gemini-code-assist]

Summary of Changes

Hello @cheriepwoo, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the documentation for integrating AWS Bedrock LLMs into Flowise by providing a comprehensive setup guide. It clarifies the steps required for users to leverage Amazon Bedrock's foundational models within their Flowise applications and also adds a helpful link to Flowise deployment instructions on AWS, streamlining the overall user experience.

Highlights

• AWS Bedrock LLM Setup: Detailed instructions for integrating and configuring AWS Bedrock Large Language Models within Flowise chatflows have been added. This includes steps for setting up AWS credentials, adding the Bedrock LLM node, configuring its inputs (credentials, region, model name), and connecting it within a chatflow.
• Flowise Deployment on AWS Link: A direct link to documentation for deploying Flowise on AWS has been included for user convenience.

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[gemini-code-assist]

Code Review

This pull request adds essential documentation for setting up the AWS Bedrock LLM node, replacing the previous placeholder content. The new guide is well-structured and provides a step-by-step process. My review includes a few suggestions to enhance the clarity and conciseness of the instructions, making them even more user-friendly.

[gemini-code-assist]

The introductory sentence could be clearer about the relationship between the Flowise node and the AWS service. Rephrasing it will improve readability for users.

Suggested change

	AWS Bedrock Large Language Model provides access to Amazon Bedrock's Foundational Models and managed service for generative AI apps.
	The **AWS Bedrock LLM** node integrates with Amazon Bedrock, a managed service that provides access to foundation models for building generative AI applications.

[gemini-code-assist]

There is a trailing whitespace at the end of this line. It should be removed for cleaner markdown.

Suggested change

	<figure><img src="../../../.gitbook/assets/image (2) (5).png" alt="" width="275"><figcaption><p>AWS Bedrock Node</p></figcaption></figure>
	<figure><img src="../../../.gitbook/assets/image (2) (5).png" alt="" width="275"><figcaption><p>AWS Bedrock Node</p></figcaption></figure>

[gemini-code-assist]

The description for AWS Credential is a bit verbose. It could be more concise and provide more specific guidance on the required IAM permissions, which would be very helpful for users setting this up for the first time.

Suggested change

	* AWS Credential: The AWS credential with your AWS access key. Select or create a new AWS credential. Ensure that your AWS credentials or IAM role has access to AWS Bedrock models and other required AWS services in your Chatflow.
	* AWS Credential: Select an existing AWS credential or create a new one. The associated IAM user or role must have permissions for `bedrock:InvokeModel` and any other required AWS services in your Chatflow.

[gemini-code-assist]

The description for Region is redundant. The second sentence repeats the information from the first. It can be simplified for better readability.

Suggested change

	* Region: The region where your AWS Bedrock resources are located. Ensure that you select the region where your AWS Bedrock models and resources are deployed.
	* Region: The AWS region where your Bedrock models are available.

[gemini-code-assist]

There are two trailing spaces at the end of the line. While this can create a hard line break in some markdown parsers, it's unnecessary here given the blank lines that follow. It's best to remove them for cleaner code.

Suggested change

	For information about deploying Flowise on AWS, see [AWS](../../../configuration/deployment/aws.md).
	For information about deploying Flowise on AWS, see [AWS](../../../configuration/deployment/aws.md).